LoRaWAN has emerged as a leading LPWAN technology for emergency response systems due to its energy efficiency and wide coverage. However, its limited bandwidth poses significant challenges for transmitting time-sensitive images, which are crucial for first responders. A major constraint in such scenarios is that the sender - typically a resource-constrained edge device - cannot perform complex Machine Learning (ML) operations for image compression or enhancement prior to transmission. Conversely, the receiver, often a more capable gateway, has sufficient computational resources to handle ML-based image reconstruction. To address this asymmetry, we propose ZML-ChunkLoRa, an adaptive image transmission framework that segments images into optimally sized chunks based on real-time network conditions. Reduce sender-side processing while enabling high-quality ML-based reconstruction at the receiver. By offloading computation to the gateway, ZML-ChunkLoRa improves transmission efficiency without violating the strict energy and bandwidth restrictions of LoRaWAN. Experimental results demonstrate that our approach significantly improves image transfer speed and visual quality in time-critical, resource-limited first-responder environments.

Distributed computing can scale the capacity of quantum computing through small quantum processors interconnected by quantum communication links. In contrast to monolithic quantum computers, it enables a large-scale, robust computing environment for practical, real-world tasks. Distributed quantum computing must overcome challenges in task distribution and resource optimization in order to obtain highfidelity results. Key tasks include managing quantum communication costs, mitigating communication noise, achieving high-fidelity entanglement, and optimizing qubit allocation. To solve these challenges, we present HiFi-DisQ, a distributed quantum computing framework that aims to provide high fidelity distributed quantum circuit performance by minimizing non-local operations, pattern-matching and circuit remapping algorithms. The proposed framework improves the circuit performance and provides faster transpilation and effective resource optimization. Our method shows$5 \%-20 \%$improvement in fidelity and$4 \%-14 \%$reduction in communication cost.

The popularity of wearable smart devices has increased due to their seamless monitoring of vital signs during daily activities. Federated learning leverages these devices along with participants’ smartphones to fine-tune pre-trained models. Moreover, calibrating the differences between wearables and smartphones in terms of sampling rates, orientations, activity correlation, battery power, and other factors is challenging. Thus, the paper introduces a participant and wearable selection cross-device federated learning approach. It leverages criteria such as the activity wearable(s) relationship, data quality, battery life, sampling rate, and so on to perform the wearable selection. The server evaluates and estimates the utility of each participant and selects those with higher utility in each communication round. We then figure out the optimal weighted contribution of each participant to perform robust aggregation. We also use knowledge distillation techniques to develop a high-performing and lightweight wearable model. Finally, we conduct simulation and real-world experiments on existing datasets and compare our approach with state-of-the-art. The result shows an improvement of$3\!\!-\!\!4\%$in accuracy via fine-tuning from selected wearable data.

Federated learning enables multiple participants to collaboratively train machine learning models without the need to share their private and limited data, thereby preserving privacy. When datasets used in federated learning contain noisy labels, it can lead to degraded performance and an increased number of communication rounds needed to achieve convergence. This, in turn, requires more time and energy to train the model. This paper proposes a federated learning approach to suppress the unequal distribution of the noisy labels in the dataset of each participant. The approach first estimates the noise ratio of the dataset for each participant and normalizes it using the server dataset. Next, the approach considers the influence of each participant and calculates the optimal weighted contributions for each one. The approach also considers bias in the server dataset and minimizes its impact on the participants. Further, the paper provides an expression to estimate the number of communication rounds required for convergence. Results demonstrate the superiority of the proposed approach over baselines in terms of communication rounds and performance.

Federated learning is a distributed training framework that ensures data privacy and reduces communication overhead to train a shared model among multiple participants. Noise in the datasets and communication channels diminishes performance and increases communication rounds for convergence. This paper proposes a federated learning approach to rectify noise in local datasets and communication channels for reducing communication rounds. We employ two filters: one to rectify noise in the dataset and the other for the communication channel. We also consider two types of participants: one uses a filter on the dataset, and the other uses both the dataset and the communication channel. We first derive the expression to estimate the communication rounds for convergence. Next, we determine the number of participants under each type. Finally, we perform the experiments to verify the effectiveness of the proposed work on existing datasets and compare them with state-of-the-art techniques.

Long-Range Wide-Area Network (LoRaWAN) is receiving wide acceptance by offering long-distance, low-power, robust, and cost-effective communication of sensory data to the server. LoRaWAN end devices open two receive windows to receive a downlink after the transmission of data to the server. However, optimizing the delay in opening these receive windows (known as RXDelay) is pivotal for efficient uplink and downlink communication. This task becomes even more challenging when application requirements change over time. To address such challenges, this paper proposes a novel approach to estimate and configure the optimal RXDelay of end devices. By leveraging the concept of the age of information at the network server, the proposed approach dynamically adapts RXDelay based on the specific demands of different scenarios, allowing for timely data transmission. Through extensive experimentation and evaluation on a real-world LoRaWAN deployment, the proposed approach demonstrates significant improvements in communication efficiency and overall network performance. Specifically, it outperforms existing methods in packet reception rate and energy efficiency for both confirmed and unconfirmed messages across diverse network conditions, while still ensuring a balanced throughput.

In IEEE 802.11-based access networks (Wi-Fi), the client remains connected to a far-poor Access Point (AP) rather than switching to a near-better AP. This scenario is termed a sticky client problem. This scenario can severely impact the performance of real-time applications. Several standards, such as 802.11k1v/r, are being developed to enhance Wi-Fi roaming capabilities. However, the sticky client problem is yet to be solved completely. This paper proposes Sticky less, a novel method that leverages machine learning to learn the home Wi-Fi network behavior to address the sticky client problem. Initially, Stickyless divides the deployment area of APs into distinct zones, generates training data, and subsequently trains the machine learning module. The Stickyless employs clustering models to recommend selecting the optimal AP within a specific zone by considering the application performance and quality metrics. To conclude, Stickyless assesses performance using a proposed scoring and cascading module. We also developed a prototype to evaluate the Stickyless performance, outperforming the existing methods. The proposed method improves the Wi-Fi roaming experience by reducing stickiness up to 40 %. Thereby, it improves the link quality of the client by an average of 19 % and decreases the packet error rate by up to 3.5 % compared to the existing approaches. We also experimented with popular gaming apps, and Stickyless reduced the latency by up to 7 -fold.

Automated feature extraction capability and significant performance of Deep Neural Networks (DNN) make them suitable for Internet of Things (IoT) applications. However, deploying DNN on edge devices becomes prohibitive due to the colossal computation, energy, and storage requirements. This paper presents a novel approach, EarlyLight, for designing and training lightweight DNN using large-size DNN. The approach considers the available storage, processing speed, and maximum allowable processing time to execute the task on edge devices. We present a knowledge distillation based training procedure to train the lightweight DNN to achieve adequate accuracy. During the training of lightweight DNN, we introduce a novel early halting technique, which preserves network resources; thus, speedups the training procedure. Finally, we present the empirically and real-world evaluations to verify the effectiveness of the proposed approach under different constraints using various edge devices.

Federated learning is an emerging paradigm that provides privacy-preserving collaboration among multiple participants for model training without sharing private data. The participants with heterogeneous devices and networking resources decelerate the training and aggregation. The dataset of the participant also possesses a high level of variability, which means the characteristics of the dataset change over time. Moreover, it is a prerequisite to preserve the personalized characteristics of the local dataset on each participant device to achieve better performance. This article proposes a model personalization-based federated learning approach in the presence of variability in the local datasets. The approach involves participants with heterogeneous devices and networking resources. The central server initiates the approach and constructs a base model that executes on most participants. The approach simultaneously learns the personalized model and handles the variability in the datasets. We propose a knowledge distillation-based early-halting approach for devices where the base model does not fit directly. The early halting speeds up the training of the model. We also propose an aperiodic global update approach that helps participants to share their updated parameters aperiodically with server. Finally, we perform a real-world study to evaluate the performance of the approach and compare with state-of-the-art techniques.

Federated Learning is a training framework that enables multiple participants to collaboratively train a shared model while preserving data privacy. The heterogeneity of devices and networking resources of the participants delay the training and aggregation. The paper introduces a novel approach to federated learning by incorporating resource-aware clustering. This method addresses the challenges posed by the diverse devices and networking resources among participants. Unlike static clustering approaches, this paper proposes a dynamic method to determine the optimal number of clusters using Dunn Indices. It enables adaptability to the varying heterogeneity levels among participants, ensuring a responsive and customized approach to clustering. Next, the paper goes beyond empirical observations by providing a mathematical derivation of the communication rounds for convergence within each cluster. Further, the participant assignment mechanism adds a layer of sophistication and ensures that devices and networking resources are allocated optimally. Afterwards, we incorporate a master-slave technique, particularly through knowledge distillation, which improves the performance of lightweight models within clusters. Finally, experiments are conducted to validate the approach and to compare it with state-of-the-art. The results demonstrated an accuracy improvement of over 3% compared to its closest competitor and a reduction in communication rounds of around 10%.

Smart spaces system equipped with sensors to collect data that can be used to generate insights about its environmental conditions. Those collected data is then transmitted to the applications to enhance the comfort, quality of life, and security of the space. Long Range (LoRa) technology provides long distance coverage and consumes low energy which makes it suitable for smart space application. There are six virtual channels to transmit data in LoRa, however network faces the interference problem when nodes transmitted data at the same time. The interference problem makes LoRa less suitable for time-critical applications. To mitigate the interference problem, a spreading factor should be allocated in an optimal way. This paper assigns the spreading factor to the LN using Rate-Monotonic scheduler to ensures data transmission within deadline with minimum energy consumption. To quantify delay in receiving the information, we use the ‘Age of Information’ metric. The proposed approach is validated using Network Simulator-3 and results show that it effectively reduces delay and energy and prolongs the network utility.

This poster presents a novel approach that harnesses large-sized deep neural networks to craft lightweight variants, addressing constraints in storage, processing speed, and task execution time on heterogeneous edge devices. Knowledge distillation is employed to refine the training of lightweight deep neural networks, and a novel early termination technique is introduced to optimize resource utilization and expedite the training process. This approach yields satisfactory accuracy while accommodating diverse heterogeneous edge device constraints.

Patient activities’ monitoring is a promising application of the Internet of Medical Things (IoMT), revolutionizing clinical diagnosis. An IoMT uses sensory data collected from smart devices to train a model on the server. The trained model recognizes the patient activities on smart devices. However, training the model on the server has raised privacy concerns and security threats. The sensitive medical data transferred from the smart devices to the Cloud poses different cybersecurity challenges, such as distributed denial of service (DDoS), phishing, network penetration, and side-channel. This article proposes a secure patient monitoring system using federated learning (FL). The system performs training on local devices and sends only weight matrices to the server for aggregation; thus, it preserves data privacy and security compromises. The system intelligently divides the participants into clusters based on the available resources, trains suitable models on each cluster, and enhances the performance via knowledge distillation (KD). The model of high-performing clusters distills knowledge to the model of small-size clusters to improve their performance. The experimental results illustrate that the proposed system successfully work in the presence of unequal resources.

Text mining in noisy situations, like poor contrast and fainted edges, is one of the challenging areas of research in the domain of social networks and computer vision. Scene text detection is a complicated task as text regionhaving varying span in term of size, orientation, aspect ratio, color, font, and script. Furthermore, the contrast of a scene image varies drastically in noisy situations due to poor illumination and image filtering. This faints the text edges and make the task of detection more challenging. In this article, we bring forward a semantic edge supervised spatial-channel attention network, known as SESANet, for detecting arbitrary-shaped text instances in noisy scene images with faint text edges. Our network learns multiscale (MS) supervised edge semantic, pixel-wise spatial structure information, and interchannel dependencies for precisely localizing the text masks in scene images with poor contrast and illumination. Our network is efficient, precise, and fast in nature. SESANet captures rich, dense, discriminative, and MS semantic information. The experimental results show the success of the proposed network. It shows a superior performance with regard to recall on the publicly available benchmark datasets. A new dataset scene images, named as Edge-fainted Noisy Arbitrary-shaped Scene Text (EFNAST) dataset, having varying noise density, poor contrast, low illumination, and faint edges is created.

Augmented intelligence is an innovative amplification of artificial intelligence that allows human experts to take over the autonomous decision of machines. It also facilitates human-intelligence-based decisions on the network edge using low-cost and small-sized devices. Augmented intelligence and the Internet of Things collectively create augmented intelligence of things. It logically and effortlessly interrelates human intelligence to articulate smart decisions. Unmanned aerial vehicles find various applications ranging from search operations during disasters to intruders identification; thus, they are suitable for aerial networks, where connections between base stations and servers are extinct. This article presents an approach to enhance the lifetime of unmanned-aerial-vehicles-enabled aerial networks via augmented intelligence. It first considers the available battery power to transform a large-size deep neural network into a lightweight. We next present a knowledge-distillation-based approach, which reduces training time and enhances accuracy. Finally, we evaluate the approach on the existing dataset.

Restrictive public health measures such as isolation and quarantine have been used to reduce the pandemic virus's transmission. With no proper treatment, older adults have been specifically advised to stay home, given their vulnerability to COVID-19. This pandemic has created an increasing need for new and innovative assistive technologies capable of easing the lives of people with special needs. Smart home systems have become widely popular in providing such assistive services to isolated older adults. These systems can provide better services to assist older people if it anticipates what activities inhabitants will perform ahead of time. For example, a smart home can prompt inhabitants to initiate essential activities like taking medicine using activity prediction. This paper proposes a multi-task activity prediction system that jointly predicts labels, locations, and starting times of future activities. The observed sequence of previous activities characterizes future activities. We use body activity information from wearable sensors and motion information from passive environmental sensors to sense activities of daily living of older adults. The activity prediction system consists of recurrent neural networks to capture temporal dependencies. This work also carries out several experiments on collected and existing real datasets to evaluate the system's performance.

Intelligent Transportation System (ITS) helps to improve vehicle health, driver safety, and passenger comfort. Remotely sharing the information of ITS to train the machine and deep learning models hamper data privacy and generate security threats to the passenger, driver, and vehicle owners. Moreover, sharing the information requires huge networking resources such as high data rate, low latency, and low packet loss. Federated learning provides privacy-preserving model training on the vehicle without sharing the information. However, due to poor annotation mechanisms, federated learning may suffer from imperfect labels. This paper proposes a federated learning approach for ITS that can handle imperfect labels in the datasets of the participants. The approach also uses a Long-Range network to provide communication efficient connectivity. The approach initially estimates class-wise centroids of the datasets at the participants and server and then identifies participants with imperfect labels using similarity scores. Such participants demand the fraction of the correctly annotated dataset at the server to improve performance. We further derive the expression for the optimal fraction of the dataset requested by a participant. We finally verify the effectiveness of the proposed approach using the existing model and publicly available dataset.

Availability of various sensors in the smartphone makes it easier and convenient to collect the data of human locomotion activities. A recognition approach can utilize this sensory data for recognizing a locomotion mode of a user such as a bicycle, bike, car, etc. Such recognition of locomotion modes helps in the precise estimation of transportation expenditure, travel time, and appropriate journey planning. The accuracy of the recognition approaches heavily relies on the training dataset having correctly annotated labels. These labels are usually assigned using crowdsourcing or web-based queries for economic and fast annotation. However, the annotation generates abundant noisy labels in the dataset. This paper proposes a locomotion mode recognition approach capable of handling noisy labels in the training dataset. The approach builds an ensemble model by developing three different deep learning-based models, namely conventional, noise adaptive, and noise corrective, to handle different concentrations of noisy labels. The ensemble model not only improves the recognition performance but also helps in estimating the concentration of noisy labels. Experimental results demonstrate the effectiveness of the proposed approach on collected and existing datasets.

Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a game-theoretic approach to estimate the time duration of using a spreading factor that ensures on-time data delivery with maximum network utilization. We incorporate the Age of Information (AoI) metric to capture the freshness of information as demanded by the applications. Our proposed approach is validated through simulation experiments, and its applicability is demonstrated for a crop protection system that ensures real-time monitoring and intrusion control of animals in an agricultural field. The simulation and prototype results demonstrate the impact of the number of nodes, AoI metric, and game-theoretic parameters on the performance of the IoT network.

With the availability of low-cost and low-power sensors, it becomes easier to assess river water quality. The existing work on water quality assessment require a large amount of correctly annotated data for training. However, in the real-world scenario, obtaining such annotated data is costly and time consuming. In this work, we propose a sensor-based river water quality assessment system using the deep neural network (DNN). The system first presents a technique to estimate the water quality index (WQI) for labeling the given lab samples. WQI is a vital matrix used to transform large quantities of water data into a single unified number. Next, we present an automatic annotation technique that assigns labels to the sensory data instances using lab data. Finally, the labeled sensory data instances are used to build a DNN classifier that predicts water quality. This work also proposes a noise handling loss function to accommodate noisy labels. We evaluate the performance of the system on the river data set of major Indian rivers. We use four performance metrics during the experiment, including precision, recall, accuracy, and$F1$score. Additionally, the system achieves an accuracy of more than 90%, despite 20% noisy labels. The code is athttps://github.com/sourcecodecselab/river_water_monitoring.

The ability to recognize human actions in a video is challenging due to the complex nature of video data and the subtlety of human actions. Human activities often get associated with surrounding objects and occur in specific scene contexts. Existing action recognition systems are incapable of separating human actions from representation biases, like co-occurring objects and underlying scene, which often dominate subtle human actions. In this paper, we address the issue of factorization of human actions into the activity performed by the actor, co-occurring objects, and underlying context to mitigate the influence of representation biases when they are irrelevant to the action in consideration. We propose a deep neural network architecture, denoted byFactorNet, for efficient action recognition in videos with long temporal duration. We design an attention mechanism that separates an actor from the associated objects and co-occurring scene followed by capturing long-range temporal context. We perform a comprehensive set of experimentation on six benchmark datasets to show the efficacy of our architecture. To train a model using recent video-based action datasets certainly capture and leverages such bias. The supervised representation may not be competent to new action classes. We therefore design a new dataset, known asFactNet, which consists of activity-object-scene related actions that occur in day-to-day applications. Dataset Link: FactNet.

Industrial IoT (IIoT) accomplishes digital manufacturing that incorporates various devices, simulators, and tools with multiple sensors. These sensors provide sufficient data to coordinate and monitor industrial systems. IIoT requires dedicated supporting devices for an application to execute a given task in maximum allowable response time. The requirement of dedicated devices increases system cost. Task containerization is a process of exploiting available resources of the host machines to meet out varying demands of different applications. It avoids the additional cost to buy dedicated IIoT devices while adding new applications. This article proposes an approach to securely process a given IIoT task within an allowable response time. We use the game theory approach to estimate the fractions of the task to be containerized on the machines. Next, the estimated fractions for each machine maximize the system utility. Finally, we illustrate the experimental results to validate the performance of the proposed approach.

Intelligent systems, like driver assistance systems, remain within vehicles and help drivers by providing essential information about traffic, blockage of roads, and possible routes for safe driving. The objective of scene text spotting in a driver assistance system is to localize and recognize scene texts, signs of milestones, traffic panels, and road marks in natural scene images. However, text edges get fainted due to adverse weather conditions, like fog, rain, smog, or poor contrast. This makes the task of spotting more challenging. In this paper, we propose an end-to-end trainable deep neural network, known asMet-MLTS, that can address the issue of spotting multi-oriented text instances in scene images captured in adverse meteorological conditions. It localizes words, predicts script class, and performs word spotting for every rotated bounding box. It is a fast multilingual scene text spotter that utilizes hierarchical spatial context, channel-wise inter-dependencies, and semantic edge supervision to localize and recognize words and predict script class in scene images using smartphones. We explore inter-class interference to reduce the misclassification problem. A light-weight recognition module for multilingual character segmentation, word-level recognition, and script identification is incorporated. We demonstrate the efficacy of our spotting network on resource-constraint devices.

Various wireless local area network technologies are developed for Internet of Things of which Long-Range Wide Area Network is preferred for low power long range communication. In the LoRa network, multiple LoRa nodes can simultaneously communicate with a LoRa gateway which causes the interference problem. This article presents an approach for estimating the association time duration between each LoRa node and the LoRa gateways for transmitting the data of end users to the LoRa gateways with high packet delivery ratio. The approach uses a Beta distribution based reputation model for estimating the association time duration between each LoRa node and LoRa gateways and Bayesian Game strategy which accommodates unknown private information of the LoRa nodes. The approach is validated by simulating the LoRa network using network simulator-3. We also demonstrate an on-campus traffic monitoring system to detect the reckless driving action and estimate the vehicle speed using sensors embedded nodes deployed along both sides of the road.

With the availability of various sensors in the smartphone, identifying a locomotion mode becomes convenient and effortless in recent years. Information about locomotion mode helps to improve journey planning, travel time estimation, and traffic management. Though there exists a significant amount of work towards locomotion mode recognition, the performance of these work is not pertinent and heavily depends on the labeled training instances. As it is impractical to gather a prior information (labeled instances) about all types of locomotion modes, the recognition model should be able to identify a new or unseen locomotion mode without having any corresponding training instance. This paper proposes a sensors based deep learning model to identify a locomotion mode by using labeled training instances. The approach also incorporates a concept of Zero-Shot learning to identify an unseen locomotion mode. The model obtains an attribute matrix based on the fusion of three semantic matrices. It also constructs a feature matrix by extracting the deep learning and hand-crafted features from the training instances. Later, the model builds a classifier by learning a mapping between attribute and feature matrices. Finally, this work evaluates the performance of the approach on collected and existing datasets using accuracy and F1 score.

A Fog computing-based transportation system envisions to reduce energy consumption and communication delay. This paper presents a Fog computing-based scheme for assisting passengers, which involves task offloading and reallocation. We consider a dynamic environment where the passengers frequently change their locations. Additionally, the scheme mitigates the sudden failure of the Fog devices. We employ a game-theoretic approach to determine optimal fractions of a task associated with the passengers to be offloaded among the Fog devices and Cloud. It also supports the reallocation of the allocated fractions of the task. This offloading and reallocation of tasks ensures the execution within a given time constraint and requires minimal execution cost. We also prove the existence of near Nash equilibrium for the allocated fractions of the task on Fog devices. Further, this work covers different possibilities of dependencies among the fractions of the task and corresponding utilities of Fog devices in the dynamic environment. Finally, we present the empirical and real-world evaluations to verify the effectiveness of the proposed scheme in terms of the number of Fog devices, the deadline of the task, and game parameters.

An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a deep learning based compression-decompression model for reducing the size of the energy time series at the Edge devices. Next, it formulates an optimization problem for finding the suitable compressed energy time series to reduce the energy consumption and delay of the system. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed time series to the operator in the given time. Our simulation and prototype results demonstrate the impact of the parameters of the compression model, network, and the number of smart meters and appliances on delay, energy consumption, and accuracy of the system.

In this paper, we develop techniques that create smart space in an efficient manner, wherein the efficiency is defined in terms of all-together: energy, security, delay, and cost. We design an energy-efficient smart space system using the Long-Range (LoRa) network. The system consists of various sensors that generate sensory data represented as Multi-dimensional Time Series (MTS). The sensors are connected with an Edge device and LoRa node for processing and transferring the MTS, respectively. The system first proposes a deep learning-based compression-decompression model for reducing the size of MTS at the Edge devices. Next, it uses game theory for finding a minimum-cost security mechanism to facilitate the secure transmission of MTS. Then, we formulate an optimization problem to obtain a suitable compression ratio and security mechanism to reduce the system's energy consumption, delay, and security cost. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed and secured MTS to the application server in the given time period with desired accuracy. We evaluate the proposed system over the simulation platform and demonstrate the impact of the parameters of the compression model, network, security mechanism, and the number of sensors on energy consumption, delay, cost, and system accuracy.

Network traffic classification has been an interesting topic of research for many years. It plays a crucial role in many network applications including resource allocation, intrusion detection, and quality of service. Network traffic is essentially a sequence or flow of time-stamped packets that are exchanged between two devices. The traffic flow also contains payload data along with information about packet statistics such as size, inter-arrival time, and direction. As these statistics are obtained from the time-stamped packets, they form a Multivariate Time Series (MTS). Such an MTS needs to be classified as early as possible to identify an Internet application associated with the generated traffic flow. In this paper, we propose an Early traffic Flow Classification (EFC) approach for identifying Internet applications using MTS. The approach estimates application-wise minimum required packets from the training data by employing k-means clustering and Long Short Term Memory model. We also develop a class forwarding method to utilize correlation that exists among different packet statistics. Additionally, we collect a real-world traffic flow dataset to evaluate the effectiveness of the approach. Experimental results show that EFC approach requires only the first 15 packets of the flow to achieve an accuracy of more than 90%.

With the expeditious evolution in technology, recent years have witnessed significant growth in passenger assistance applications in the transportation system. Such applications have varying demands for resources and quality of services. This paper presents a Fog computing based transportation system. The system uses multiple Fog devices to provide assistance to the passengers. The passengers and vehicles work as end-users and the Edge devices in the system, respectively. A passenger generates a task and using the Edge device forwards it to the Fog devices for further processing. Selected Fog devices parallel process the fraction of the task, so that the complete task processes within the given time constraint. We use the gamma function based reputation model of Fog devices, which provides the confidence to complete a given task successfully. We present a Knapsack based task offloading algorithm, which helps to fully utilize the resources of the Fog devices. We also present a competitive game model and near Nash Equilibrium solution for estimating the optimal value of the fraction of the task process at Fog devices. Finally, we develop a prototype and present results to investigate the performance of the propose system.

Coronavirus outbreak is one of the challenging pandemics for the entire human population on Earth. Techniques, such as the isolation of infected people and maintaining social distancing, are the only preventive measures against the pandemic. The actual estimation of the number of infected peoples with limited data is an indeterminate problem faced by data scientists. There are several techniques in the existing literature, including reproduction number and case fatality rate, for predicting the duration of a pandemic and infectious population. This article presents a case study of different techniques for analyzing, modeling, and representing the data associated with a pandemic such as COVID-19. We further propose an algorithm for estimating infection transmission states in a particular area. This work also presents an algorithm for estimating end time of a pandemic from the susceptible infectious and recovered model. Finally, this article presents the empirical and data analysis to study the impact of transmission probability, rate of contact, infectious, and susceptible population on the pandemic spread.

Getting real-time information about the operational behavior of industrial or domestic appliances becomes effortless with the availability of sensors. The sensors generate multiple streams of measurements, called as multivariate time series, corresponding to an operation of the appliance. An appliance can go through various types of faults during its lifetime. Such a fault can be identified by classifying the multivariate time series (MTS), which is generated by the sensors corresponding to this fault. As it is also unfeasible to have prior knowledge about all types of faults, the classification approach should also be able to identify an unseen (unknown) fault using its MTS. In this article, we propose a semantic-information-based early classification approach for MTS. The approach uses a concept of zero-shot learning to classify an unseen fault. This work conducts a case study to evaluate the approach by classifying different faults of a washing machine using sensory data.

Activity classification has been an interesting area of research for many years, to better understand human behavior. Recent advancements in embedded computing systems allowed the emergence of several state-of-art solutions for human activity classification using sensors of a smartphone. The sensors generate temporal sequences of observations for human activity, which is called as Multivariate Time Series (MTS). Current state-of-art solutions for human activity classification suffer from two major limitations: first, the length of testing MTS should be equal to the training MTS and second, the MTS should not have any faulty time series. In real-time applications, it is desirable to classify a human activity using an incomplete MTS as early as possible. In this work, we propose a fault-tolerant early classification of MTS (FECM) approach to address these limitations. FECM builds a set of classification models using MTS training dataset. The approach employs Gaussian Process classifier to estimate minimum required length of time series, which is used to predict a class label of new MTS. Further, FECM uses an Auto Regressive Integrated Moving Average model to identify faulty time series in the new MTS. Finally, we conduct an experiment to evaluate the performance of FECM using accuracy and earliness metrics.

Long Range (LoRa) is a wireless communication technology which enables Internet of Things (IoT) devices to efficiently and robustly communicate over long distances with low power consumption. The LoRa supports six Spreading Factors (SFs) and therefore a limited number of virtual channels are possible at a given time instance. Such limited number of channels restate the simultaneous use of a LoRa Gateway (LG) by the large number of LoRa Nodes (LNs) in a dense LoRa network and create the bottleneck problem at the LG. The bottleneck problem reduces the proper use of the allocated SFs and the utility of the LNs. In this paper, we propose a game theoretic approach that allocates the time duration to the LNs in the network for accessing the SFs. Such time duration maximizes the utility of the LNs in the network. The time duration determined on different SFs are then scheduled to minimize the waiting time of LNs. The proposed approach is validated by simulating the LoRa network using Network Simulator-3. Our simulations show that the proposed approach effectively reduces the waiting time and prolongs the network utility.

Recent years have witnessed unprecedented growth in sensors-based indoor activity recognition. Further, a significant improvement in recognition performance of indoor activities is observed by incorporating Deep Neural Network (DNN) model. In this paper, we propose knowledge distillation based economic and efficient indoor activity recognition approach for low-cost resource constraint devices. Here, we adopt knowledge from teacher and trainee (cumbersome DNN models) for training student (compressed DNN model). Initially, student and trainee both are beginner and trainee helps the student in learning from the teacher. The student, after certain steps, is mature enough for directly learning from the teacher. We introduce an early halting mechanism for simultaneously reducing floating-point operations and training time of the student model.

In the era of the Internet of Things (IoT), the sensor-based devices produce the Multivariate Time Series (MTS). A classification approach helps to predict the class label of an incoming MTS. Due to the large dimension and different sampling rate of the sensors in a given MTS, a classifier takes time to predict the class label. Some IoT applications may require early prediction of the class label where the classifier starts the prediction once the minimum number of data points are collected. In this article, we address the problem of early prediction of the class label of an MTS in IoT. This work considers the sensors with different sampling rate to generate the MTS. Each sensor generates a time series (component) of the MTS. We propose a Divide-and-Conquer–based early classification approach for classifying such MTS. The approach constructs an ensemble classifier using a probabilistic classifier and hierarchical clustering. The ensemble classifier employs a Divide-and-Conquer method to handle the different sampling rate components during the prediction of class label. The experimental results show that our approach significantly outperforms the existing approaches on real-world datasets using various evaluation metrics.

An important issue of research in the transportation system is timely classification of the inside-outside environment of the vehicle using sensors. The sensors generate the multivariate time series data, which requires a classification technique to classify it in real-time. Road surface classification is an example, where multivariate time series data can be used for early identification of the type of road surface. The challenge is to maintain the accuracy of the classification using a minimum number of data points of the multivariate time series. This work proposes an early classification approach for multivariate time series with a desired level of accuracy. It is assumed that the number of samples in the time series are not equal for a given period of time due to different type of sensors. Gaussian Process learning method is used to first estimate the minimum required length of the time series which helps to build an ensemble classifier with a desired level of accuracy. The ensemble classifier is used to predict the class label of an incoming multivariate time series. This work demonstrates a road surface classification system using the built ensemble classifier. Finally, the ensemble classifier is also evaluated on the various existing datasets from other domains. The results demonstrate the significance of early classification approach using accuracy, earliness, and confusion matrix, with the minimum required data points.

Wireless Local Area Networks (WLANs) are one of the most popular networks for the Internet-of-Things (IoT) applications. Among various WLAN technologies, the Long-Range WAN (LoRaWAN) has gained a high demand in recent years because of its low power consumption and long-range communication. However, the Long-Range (LoRa) network suffers from interference problem among LoRa Devices (LDs) that are connected to the LoRa gateway by using the same Spreading Factors (SFs). In this article, we propose a game theory-based approach for estimating the time duration of transmission of data on suitable SFs such that interference problem is reduced and network devices maximize their utilities. We next propose a scheduling algorithm that schedules the allocated time duration on the SFs such that the waiting time of the network can be minimized. We finally use the network simulator-3 for validating the propose work. Various experiments are performed which demonstrate the improvement in the network performance.

Ubiquitous Computing (UbiComp) is a computational paradigm that enhances the use of computing devices by making them available to the user anywhere and anytime. From the energy perspective, it is often very important to compute the entire UbiComp task within a specific deadline with minimum energy. The literature on determining the energy consumption of the system for computing the task does not consider periodic tasks and different sampling rate of the sensors, which eliminates the deadline constraints in the analysis. Since the period of the tasks is not fixed, the estimated delay without considering the fixed period is lower than the actual value. In this paper, we assume that an Edge, Fog, and Cloud layers based UbiComp system computes the periodic task within the specific deadline. We derive the expressions of total delay and energy consumption of the UbiComp system. Using the derived expressions, we estimate fractions of the task that are computed at each layer to reduce the energy consumption such that the task is computed within a specific deadline. Our numerical and prototype results demonstrate the impact of the data size, network topologies, deadline, and characteristics of the sensors on the energy consumption, delay, and accuracy of the system.

Classification of Multivariate Time Series (MTS) data has been an important area of research for many years. In time-critical applications, such as health informatics, fire detection, and disaster forecasting, it is desirable to classify the MTS data as early as possible. This work proposes an early classification approach to classify an incoming MTS. The early classification approach helps to predict the class label of an incoming MTS without waiting for the full length. Different from the existing work, this work considers that sampling rate of the sensors which generated the MTS is different. The performance of the approach is evaluated on a publicly available dataset using accuracy, earliness and energy consumption.

Internet of Things (IoT) finds their applications in many areas that include environmental monitoring, industrial control, traffic congestion control, smart metering, and smart parking. An important issue of research in IoT is to successfully transmit the data to the cloud and yet minimize the energy consumption of the battery-powered IoT devices. Long Range (LoRa) is a long-range wireless communication protocol that competes against other low-power wide-area networks. In this paper, we propose a Stackelberg Game based model for allocation of the appropriate power levels to the LoRa nodes in energy-efficient LoRa network. The utility of the network server, works as a leader player, is to successfully receive the data from the LoRa nodes. The utility of the LoRa nodes, work as followers player, is to reduce the power consumption during transmission of the data to the LoRa gateway. Simulation results evaluate the performance of the proposed game model to validate its effectiveness.

Water is one of the necessary things required for living. Sensor networks play a major role in detecting the pollution level of the river water. Detection of the sudden changes in the pollution level of the river water is a challenging problem because it requires continuous monitoring of the river water. In this paper, we propose a Stackelberg Game based system to detect the sudden changes of the pollution level of the river water. We use Long Range (LoRa) technology for transferring the sensory data to the server. The LoRa uses different Spreading Factors which helps to reduce the communication energy and enhances the lifetime of the system. We demonstrate the utility of the system and study the impact of the sudden changes of the pollution level of the water on the energy consumption of the system.

Scene text detection is an important as well as challenging problem in computer vision. Text information plays an important role in scene understanding, image indexing, and indoor navigation. Deep neural networks are being widely used due to their capability to learn strong text features. However, the state-of-the-art scene text detection methods capture weak text features in the early layers with no scope to improve the captured features. In this paper we propose a novel recurrent architecture to improve the learnings of a feature map at a given time, by using global and local information from the same feature map at the previous time, for detecting occluded texts and long words. We design the recurrent text convolutional layer for seamless integration of recurrent architecture with local-global map. The experimental results on publicly available scene text datasets show the efficiency of our framework. We also create our own scene text dataset.

Scene text detection systems detect texts in natural scene images. Hazy scene text detection is a specific case of scene text detection where detection is done in hazy weather conditions. Haze affects the contrast of the image. In this paper, we reframe the traditional two class hazy scene text detection problem into a four class problem. We develop a deep learning based model that combines features from all layers for accurate and fast text detection from hazy images. In addition, we develop a novel training approach for the four class problem. Merging and patch-NMS are used as post processing steps for fast word detection. We also create a new dataset of hazy scene images and obtain significant improvements on an existing hazy scene text dataset.

Scene text detection has attracted great interest from the computer vision and pattern recognition communities since text information plays an important role in image indexing and scene understanding. Deep neural networks have become popular for the task of scene text detection, especially for their ability to learn strong text features. However, existing deep learning based state-of-the-art scene text detection methods detect texts only from a single feature map which is unable to capture semantic information at all scales. In this paper, we propose a novel deep learning based model that leverages the pyramid structure of feature maps for accurate scene text detection. We also design a deep convolutional neural network model for non-maximum suppression. In addition, we develop a novel loss function and training method for end-to-end training. The experimental results validate that our end-to-end system is simple, fast, and achieves high accuracy on standard datasets, namely, ICDAR 2015 and MSRA-TD500. We also create a dataset for scene text detection.

Recent advances in sensor technology and ubiquitous computing have sustained them as a better alternative for kids activity monitoring system. This paper presents a system that continuously monitors the proper pencil grip and writing activity of kid using accelerometer and pressure sensors. The system creates a labeled dataset for recognizing the correct location of pencil grip, holding direction of the pencil, and writing activity of kids. The system uses a supervised machine learning technique and labeled dataset for classifying whether a kid properly uses a pencil or not. In addition, a prototype system is developed and adequately tested on real-time user data. The prototype uses accelerometer and pressure sensors for extracting the writing activities. The system uses bluetooth low energy for wirelessly transferring the sensed data of writing activities to the parent and teachers. The system is suitable for kids due to its low cost, small size, and low-power consumption.

Animation of videos is a modern form of art. The processing of videos of natural scenes is a great way to produce fast and aesthetically pleasing animations. However, the animation process leads to loss of many details in the produced video. In addition, the process of animating a video is time-consuming, especially in resource constrained devices like smart devices. In this paper, we propose a novel framework to animate videos while preserving their text content. A multiplayer simultaneous extensive-form game based on Markov decision process is used to classify the candidate text regions either as text or non-text. A fast animation technique using optimized glass painting is designed to efficiently animate videos with scene text using smart devices. We have used two public datasets, namely, ICDAR 2013 and Hua's video datasets as well as our own dataset created from videos available from Google, to test our framework. The framework is implemented in different smart devices to show its efficiency.

Recent advances in robotics and low-power embedded systems made three-dimensional (3D) mobile wireless sensor networks (MSNs) an effective solution for monitoring a field of interest (FoI). From a cost perspective, it is often important to ensure the desired coverage ratio for the FoI within a maximum allowable response (MAR) time, by using a minimum number of sensors in MSNs. The literature on determining the minimum number of sensors for the desired coverage ratio assumes that the FoI is unbounded to overcome the border effects. Since the entire sensing sphere of the sensors near the boundary may not be useful for the coverage, the number of sensors estimated without the border effects is lower than the actual value. In this paper, we estimate the minimum number of sensors required to achieve a desired coverage ratio in a given MAR time for a 3D FoI. We term this problem (α; V; T)-coverage problem, where a, V , and T are the desired coverage ratio, average speed of sensors, and MAR time, respectively. We assume straight line mobility model for the sensors and consider the border effects while deriving the expected sensing volume of a sensor useful in coverage. We also consider the restriction of sampling rate of the sensors in this analysis. We discuss the application of our analysis for a non-hyper-rectangle shaped FoI, random walk, and waypoint mobility models, and also the impact of neglecting the border effects. Our numerical and simulation results demonstrate the significance of border effects on the number of sensors and also the relationship between the coverage ratio, MAR time, sampling period, and the sensing range.

A recognition system is very useful to recognize human, object, and animals. An animal recognition system plays an important role in livestock biometrics, that helps in recognition and verification of livestock in case of missed or swapped animals, false insurance claims, and reallocation of animals at slaughter houses. In this research, we propose a fast and cost-effective animal biometrics based cattle recognition system to quickly recognize and verify the false insurance claims of cattle using their primary muzzle point image pattern characteristics. To solve this major problem, users (owner, parentage, or other) have captured the images of cattle using their smart devices. The captured images are transferred to the server of the cattle recognition system using a wireless network or internet technology. The system performs pre-processing on the muzzle point image of cattle to remove and filter the noise, increases the quality, and enhance the contrast. The muzzle point features are extracted and supervised machine learning based multi-classifier pattern recognition techniques are applied for recognizing the cattle. The server has a database of cattle images which are provided by the owners. Finally, One-Shot-Similarity (OSS) matching and distance metric learning based techniques with ensemble of classifiers technique are used for matching the query muzzle image with the stored database.A prototype is also developed for evaluating the efficacy of the proposed system in term of recognition accuracy and end-to-end delay.

Digital watermarking has received much attention in recent years as a promising solution to copyright protection. Video watermarking in compressed domain has gained importance since videos are stored and transmitted in a compressed format. This decreases the overhead to fully decode and re-encode the video for embedding and extraction of the watermark. High Efficiency Video Coding (HEVC/H.265) is the latest and most efficient video compression standard and a successor to H.264 Advanced Video Coding. In this article, we propose a robust watermarking framework for HEVC-encoded video using informed detector. A readable watermark is embedded invisibly in P frames for better perceptual quality. Our framework imposes security and robustness by selecting appropriate blocks using a random key and the spatio-temporal characteristics of the compressed video. A detail analysis of the strengths of different compressed domain features is performed for implementing the watermarking framework. We experimentally demonstrate the utility of the proposed work. The results show that the proposed work effectively limits the increase in video bitrate and degradation in perceptual quality. The proposed framework is robust against re-encoding and image processing attacks.

We present a mesh network for mobile (non static) devices using Bluetooth Low Energy (BLE). We propose a weight balancing technique to optimize the communication to the entire mesh network. The proposed mesh network has been evaluated by well-designed practical experiments to transmit the data. The preliminary results show that the BLE mesh network consumes less power and can transfer data with low latency.

Coverage and connectivity are important metrics used to evaluate the quality of service of wireless sensor networks (WSNs) monitoring a field of interest (FoI). Most of the literature assumes that the sensors are deployed directly in the FoI. In this paper we assume that the sensors are stochastically deployed outside the FoI. For such WSNs, we derive probabilistic expressions for k-coverage and connectivity using exact geometry. We validate our analysis and demonstrate its utility to estimate the minimum number of sensors required for a desired level of coverage and connectivity. We also demonstrate an on-campus traffic monitoring system to count the number of vehicles, detect the direction of vehicle, and to identify the vehicle (two-wheeler or four-wheeler) using sensors along both sides of the road.

Coverage is an important metric used to measure the quality of service of wireless sensor networks monitoring a field of interest (FoI). Existing literature on the coverage problem assumes that the sensors are deployed directly in the FoI. These results cannot be applied in some applications like canal water surface monitoring, because the sensors cannot be deployed on the water surface. In this paper, we analyze the coverage problem in applications where, the sensors are deployed uniformly at random outside the FoI near the boundary. We derive the expected value of the effective sensing area useful for k-coverage of the FoI using exact geometry. We demonstrate the utility of the analysis in estimation of the minimum number of sensors required for a desired level of coverage. With numerical results we show the impact of various parameters on the number of sensors.

Coverage is an important metric to measure the quality of service of a wireless sensor network monitoring a field of interest (FoI). From an energy perspective, it is often very important to maintain the desired coverage ratio with a minimum number of sensors. The literature on determining the critical sensor density (CSD) for the desired coverage ratio assumes that the FoI is unbounded or toroidal in shape. Although it is not a realistic assumption, it eliminates the border effects in analysis. Since the entire sensing area of the sensors near the boundary may not be useful for the coverage, the CSD estimated without the border effects is lower than the actual value. In this paper, we assume that the sensors are deployed uniformly at random in a convex polygon-shaped FoI and consider the border effects to derive the expected sensing area of a sensor used in the coverage. Next, we estimate the CSD required for the desired coverage ratio. We validate the analysis and demonstrate the impact of border effects on CSD using numerical results. Results show that our approach estimates the CSD better than another one that does not consider the exact geometry of the FoI.

A heterogeneous wireless sensor network (WSN) consists of sensors with unequal ranges of sensing and/or communication. In a dense WSN, a part of the region covered by a sensor may also be covered redundantly by a neighbouring sensor. In this paper, we analyse the redundancy in the coverage of a heterogeneous WSN and define the redundancy degree of a sensor. We follow a probabilistic approach to derive the expected redundancy degree of a sensor with a given number of sensors of each type in the neighbourhood. We demonstrate the accuracy of the analysis, and study the impact of the number of sensors of different types on the expected redundancy degree with numerical and simulation results. We also demonstrate an application of the redundancy analysis in the design of a heterogeneous WSN. We propose an algorithm to determine the minimum number of sensors of different types required to satisfy the desired coverage ratio and simultaneously minimise the cost of the network.